A CLI tool for generating interview questions by difficulty level and quantity, with support for multiple output formats including text, JSON, and arrays.

Randomly routes your Minecraft connection to servers from a predefined list!

An ios application that can help to randomly pick a name and is applicable to all kinds of situations requiring fair and random selection.

The aim of this project is to achieve maximum success by the algorithm in the rock-paper-scissors game with the reinforcement learning.

Generates two clash-free class schedules using a genetic algorithm with random selection, crossover, and mutation. Designed for 3 teachers over 3 days and 3 periods.

This program uses Python's random module to generate random integers and make random choices. It generates random values in different ranges and simulates a coin flip and dice roll, showcasing the randint() and choice() functions.

Can't decide? 🤷 Well, The CHOOSINATOR loooooooooves choosing, feel free to let it choooooose for you.

The purpose of this study is to predict which ad will be the most preferred by the customers over the fictitious ads clicked by the users.

Similar to a wheel for random selection, but presented as a list

Truth and Dare - Spinning Bottle Game:

This repository contains the source code of research paper titled: "A Generalized Quantum Algorithm for Assuring Fairness in Random Selection Among 2ⁿ Participants."

An efficient random item sampler that ensures O(1) sampling complexity, and equal selection probability for all items across cycles. Each cycle ensures unique, non-repeating item selections, with each item sampled only once per cycle. Upon cycle completion, the sampler automatically refreshes, initiating a new cycle to repeat the process.

Optimal pathfinding with Q-Learning and Double Q-Learning in large grids, using reward shaping and decaying epsilon; compares to Dijkstra. 🐙

Selects a Random Wishlisted Game from your Steam Wishlist.

Implementation of Q-Learning and Double Q-Learning for optimal pathfinding in large, dynamic environments. Uses reward shaping and adaptive exploration. Compares RL performance with Dijkstra and random selection, showing Q-Learning's scalability and superior cumulative rewards.